System and method for image and video encoding artifacts reduction and quality improvement

ABSTRACT

Reducing artifacts and improving quality for image and video encoding is performed in one pass to preserve natural edge smoothness and sharpness. To reduce artifacts and improve quality, several steps are implemented including spatial variation extraction, determining if a block is flat or texture/edge, classifying the pixels as texture or noise, detecting a dominant edge, checking the spatial variation of neighboring blocks, generating base weights, generating filter coefficients, filtering pixels and adaptive enhancement. A device which utilizes the method of reducing artifacts and improving quality achieves higher quality images and/or video with reduced artifacts.

FIELD OF THE INVENTION

The present invention relates to the field of image/video processing.More specifically, the present invention relates to enhancing theimage/video processing by reducing artifacts and improving quality.

BACKGROUND OF THE INVENTION

A video sequence consists of a number of pictures, usually calledframes. Subsequent frames are very similar, thus containing a lot ofredundancy from one frame to the next. Before being efficientlytransmitted over a channel or stored in memory, video data is compressedto conserve both bandwidth and memory. The goal is to remove theredundancy to gain better compression ratios. A first video compressionapproach is to subtract a reference frame from a given frame to generatea relative difference. A compressed frame contains less information thanthe reference frame. The relative difference can be encoded at a lowerbit-rate with the same quality. The decoder reconstructs the originalframe by adding the relative difference to the reference frame.

A more sophisticated approach is to approximate the motion of the wholescene and the objects of a video sequence. The motion is described byparameters that are encoded in the bit-stream. Pixels of the predictedframe are approximated by appropriately translated pixels of thereference frame. This approach provides an improved predictive abilityover a simple subtraction approach. However, the bit-rate occupied bythe parameters of the motion model must not become too large.

In general, video compression is performed according to many standards,including one or more standards for audio and video compression from theMoving Picture Experts Group (MPEG), such as MPEG-1, MPEG-2, and MPEG-4.Additional enhancements have been made as part of the MPEG-4 part 10standard, also referred to as H.264, or AVC (Advanced Video Coding).Under the MPEG standards, video data is first encoded (e.g. compressed)and then stored in an encoder buffer on an encoder side of a videosystem. Later, the encoded data is transmitted to a decoder side of thevideo system, where it is stored in a decoder buffer, before beingdecoded so that the corresponding pictures can be viewed.

MPEG is used for the generic coding of moving pictures and associatedaudio and creates a compressed video bit-stream made up of a series ofthree types of encoded data frames. The three types of data frames arean intra frame (called an I-frame or I-picture), a bi-directionalpredicated frame (called a B-frame or B-picture), and a forwardpredicted frame (called a P-frame or P-picture). These three types offrames can be arranged in a specified order called the GOP (Group OfPictures) structure. I-frames contain all the information needed toreconstruct a picture. The I-frame is encoded as a normal image withoutmotion compensation. On the other hand, P-frames use information fromprevious frames and B-frames use information from previous frames, asubsequent frame, or both to reconstruct a picture. Specifically,P-frames are predicted from a preceding I-frame or the immediatelypreceding P-frame.

Besides MPEG standards, JPEG is used for the generic coding of stillpicture. Since the encoding of still picture can be considered as theencoding of an I frame in video, no introduction of JPEG will beprovided here. There are some other proprietary methods for image/videocompression. Most of them adopt similar technologies as MPEG and JPEG.Basically, each picture is separated into one luminance (Y) and twochrominance channels (also called color difference signals Cb and Cr).Blocks of the luminance and chrominance arrays are organized into“macroblocks,” which are the basic unit of coding within a frame. Blockbased transformation and quantization of transform coefficients are usedto achieve high compression efficiency.

Since quantization is a lossy process, the combination of block-basedtransform and quantization is able to generate perceptually annoyingartifacts such as ringing artifacts and blocking artifacts. Since codingartifact reduction is fundamental to many image processing applications,it has been investigated for many years. Many post-processing methodshave been proposed. In general, most methods focus on blocking artifactsreduction or ringing artifacts reduction. Although some methods showgood results on selected applications, the quality is not high enough onnew digital HDTV. As a result, either the artifacts are still visible orthe texture detail is blurred.

Recently, more and more families are replacing their old CRT televisionwith large screen LCD or Plasma televisions with high definition. Whilethe new technologies provide better experience with higher resolutionand more detail, they also reveal more obvious artifacts and noise ifthe contents are not high enough quality. For instance, displaying aYouTube® video clip on the HDTV will show very ugly coding artifacts.

SUMMARY OF THE INVENTION

Reducing artifacts and improving quality for image and video encoding isperformed in one pass to preserve natural edge smoothness and sharpness.To reduce artifacts and improve quality, several steps are implementedincluding spatial variation extraction, determining if a block is flator texture/edge, classifying the pixels as texture or noise, detecting adominant edge, checking the spatial variation of neighboring blocks,generating base weights, generating filter coefficients, filteringpixels and adaptive enhancement. A device which utilizes the method ofreducing artifacts and improving quality achieves higher quality imagesand/or video with reduced artifacts.

In one aspect, a method of reducing image/video encoding artifacts andimproving quality within a computing device comprises classifying eachblock in an image/video using pixel intensity range-based blockclassification, detecting a dominant edge using vector difference-baseddominant edge detection, generating content adaptive base weights usingcontent adaptive base weight generation, generating content adaptivefilter weights using content adaptive final filter weight generation andenhancing textures and edges using adaptive texture and edgeenhancement. Classifying each block includes classifying a block as aflat block by comparing an intensity range with a threshold anddetermining the intensity range is less than or equal to the threshold.Classifying each block includes classifying a block as a texture/edgeblock by comparing an intensity range with a threshold and determiningthe intensity range is greater than the threshold. A pixel intensityrange-based block classification includes: determining a maximumintensity pixel value within a current block, determining a minimumintensity pixel value within the current block and calculating a spatialvariation by subtracting the minimum intensity pixel value from themaximum intensity pixel value. The method further comprises classifyingeach pixel in a current block. Classifying each pixel comprises:calculating a median of an intensity range within the current block,attributing a pixel in the current block to a first class if the pixel'sintensity value is larger than the median and attributing the pixel inthe current block to a second class if the pixel's intensity value isless than or equal to the median. Classifying each pixel furthercomprises comparing a pixel's class with a class of neighboring pixelsand denoting the pixel as noisy if the pixel's class and the class ofthe neighboring pixels is the same. The vector difference-based dominantedge detection includes calculating a vector difference between twopixels. The vector difference-based dominant edge detection includes:determining a minimum difference among four directional differences,selecting a maximum difference with a direction orthogonal to theminimum difference direction, calculating an edge difference anddetermining if a pixel belongs to a dominant edge. The content adaptivebase weight generation includes calculating base weights for noisypixels, edge pixels and texture pixels. The content adaptive base weightgeneration includes calculating base weights for a texture pixel withflat neighbor blocks. The content adaptive base weight generationincludes adjusting base weights for chrominance blocks. The contentadaptive final filter weight generation includes calculating a finalweight for a texture/edge block. The content adaptive final filterweight generation includes calculating a final weight for a flat block.The adaptive texture and edge enhancement includes applying sharpnessenhancement on denoted edge/texture pixels. The adaptive texture andedge enhancement includes: not applying sharpness enhancement if acurrent pixel belongs to a flat block, not applying sharpnessenhancement if the current pixel is a noisy pixel, enhancing sharpnessif the current pixel belongs to a dominant edge and enhancing sharpnessif the current pixel has not been filtered. The computing device isselected from the group consisting of a personal computer, a laptopcomputer, a computer workstation, a server, a mainframe computer, ahandheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, an iPod®, a video player, a DVDwriter/player, a television and a home entertainment system.

In another aspect, a method of reducing image/video encoding artifactsand improving quality within a computing device comprises classifyingeach block within an image/video, determining filter coefficients foreach pixel in a current block, filtering each pixel with the filtercoefficients to remove noise and applying an adaptive enhancement toenhance texture and edge pixels. Classifying each block includesclassifying a block as one of a flat block and a texture/edge block.Classifying each block includes classifying a block as the flat block bycomparing an intensity range with a threshold and determining theintensity range is less than or equal to the threshold. Classifying eachblock includes classifying a block as the texture/edge block bycomparing an intensity range with a threshold and determining theintensity range is greater than the threshold. Determining the intensityrange of the pixel includes: determining a maximum intensity pixel valuewithin a current block, determining a minimum intensity pixel valuewithin the current block and calculating a spatial variation bysubtracting the minimum intensity pixel value from the maximum intensitypixel value. The method further comprises classifying each pixelcomprising calculating a median of an intensity range within the currentblock, attributing a pixel in the current block to a first class if thepixel's intensity value is larger than the median and attributing thepixel in the current block to a second class if the pixel's intensityvalue is less than or equal to the median. Classifying each pixelfurther comprises comparing a pixel's class with a class of neighboringpixels and denoting the pixel as noisy if the pixel's class and theclass of the neighboring pixels is the same. Determining the filtercoefficients includes detecting a dominant edge and classifying pixelintensity for a texture/edge block. Determining the filter coefficientsincludes determining intensity difference between the current pixel andneighboring pixels for a flat block. Detecting the dominant edgeincludes calculating a vector difference between two pixels. Detectingthe dominant edge includes: determining a minimum difference among fourdirectional differences, selecting a maximum difference with a directionorthogonal to the minimum difference direction, calculating an edgedifference and determining if a pixel belongs to the dominant edge.Applying the adaptive enhancement includes applying sharpnessenhancement on denoted edge/texture pixels. Applying the adaptiveenhancement includes: not applying sharpness enhancement if a currentpixel belongs to a flat block, not applying sharpness enhancement if thecurrent pixel is a noisy pixel, enhancing sharpness if the current pixelbelongs to a dominant edge and enhancing sharpness if the current pixelhas not been filtered. The computing device is selected from the groupconsisting of a personal computer, a laptop computer, a computerworkstation, a server, a mainframe computer, a handheld computer, apersonal digital assistant, a cellular/mobile telephone, a smartappliance, a gaming console, a digital camera, a digital camcorder, acamera phone, an iPod®, a video player, a DVD writer/player, atelevision and a home entertainment system.

In another aspect, a system for reducing image/video encoding artifactsand improving quality implemented with a computing device comprises aspatial variation extraction module configured for extracting spatialvariation information for each block of an image/video, a blockdetection module operatively coupled to the spatial variation extractionmodule, the block detection module configured for classifying each blockusing the spatial variation information, a classification moduleoperatively coupled to the spatial variation extraction module, theclassification module configured for classifying each pixel in a currentblock using the spatial variation information, a dominant edge detectionmodule operatively coupled to the block detection module, the dominantedge detection module configured for detecting a dominant edge, aneighbor block check module operatively coupled to the dominant edgedetection module, the neighbor block check module configured forchecking spatial variation of neighboring blocks of the current block, abase weight generation module operatively coupled to the neighbor blockcheck module, the base weight generation module configured forgenerating base weights by combining results from detecting the dominantedge and checking the spatial variation of neighboring blocks, a filtercoefficients generation module operatively coupled to the base weightgeneration module, the filter coefficients generation module configuredfor generating filter coefficients, a pixel-based filtering moduleoperatively coupled to the filter coefficients generation module, thepixel-based filtering module configured for filtering pixels and anadaptive enhancement module operatively coupled to the pixel-basedfiltering module, the adaptive enhancement module configured forapplying an adaptive enhancement to enhance texture and edge pixels. Theblock detection module classifies a block as a flat block by comparingan intensity range with a threshold and determining the intensity rangeis less than or equal to the threshold. The block detection moduleclassifies a block as a texture/edge block by comparing an intensityrange with a threshold and determining the intensity range is greaterthan the threshold. The spatial variation information comprises anintensity range. Determining the intensity range of the pixel includes:determining a maximum intensity pixel value within a current block,determining a minimum intensity pixel value within the current block andcalculating a spatial variation by subtracting the minimum intensitypixel value from the maximum intensity pixel value. The classificationmodule classifies each pixel by calculating a median of an intensityrange within the current block, attributing a pixel in the current blockto a first class if the pixel's intensity value is larger than themedian and attributing the pixel in the current block to a second classif the pixel's intensity value is less than or equal to the median.Classifying each pixel further comprises comparing a pixel's class witha class of neighboring pixels and denoting the pixel as noisy if thepixel's class and the class of the neighboring pixels is the same. Thedominant edge detector module detects the dominant edge by calculating avector difference between two pixels. The dominant edge detector moduledetects the dominant edge by determining a minimum difference among fourdirectional differences, selecting a maximum difference with a directionorthogonal to the minimum difference direction, calculating an edgedifference and determining if a pixel belongs to the dominant edge. Thebase weight generation module calculates the base weights for noisypixels, edge pixels and texture pixels. The base weight generationmodule calculates the base weights for a texture pixel with flatneighbor blocks. The base weight generation module adjusts the baseweights for chrominance blocks. The adaptive enhancement modules appliessharpness enhancement on denoted edge/texture pixels. Applying theadaptive enhancement includes: not applying sharpness enhancement if acurrent pixel belongs to a flat block, not applying sharpnessenhancement if the current pixel is a noisy pixel, enhancing sharpnessif the current pixel belongs to a dominant edge and enhancing sharpnessif the current pixel has not been filtered. The spatial variationextraction module, the block detection module, the classificationmodule, the dominant edge detection module, the neighbor block checkmodule, the base weight generation module, the filter coefficientsgeneration module, the pixel-based filtering module and the adaptiveenhancement module are implemented in software. The spatial variationextraction module, the block detection module, the classificationmodule, the dominant edge detection module, the neighbor block checkmodule, the base weight generation module, the filter coefficientsgeneration module, the pixel-based filtering module and the adaptiveenhancement module are implemented in hardware. The spatial variationextraction module, the block detection module, the classificationmodule, the dominant edge detection module, the neighbor block checkmodule, the base weight generation module, the filter coefficientsgeneration module, the pixel-based filtering module and the adaptiveenhancement module are implemented in at least one of software, firmwareand hardware. The computing device is selected from the group consistingof a personal computer, a laptop computer, a computer workstation, aserver, a mainframe computer, a handheld computer, a personal digitalassistant, a cellular/mobile telephone, a smart appliance, a gamingconsole, a digital camera, a digital camcorder, a camera phone, aniPod®, a video player, a DVD writer/player, a television and a homeentertainment system.

In another aspect, a device comprises a memory for storing anapplication, the application configured for: extracting spatialvariation information for each block of an image/video, classifying eachblock using the spatial variation information, classifying each pixel ina current block using the spatial variation information, detecting adominant edge, checking spatial variation of neighboring blocks of thecurrent block, generating base weights by combining results fromdetecting the dominant edge and checking the spatial variation ofneighboring blocks, generating filter coefficients, filtering pixelsusing the filter coefficients and applying an adaptive enhancement toenhance texture and edge pixels and a processing component coupled tothe memory, the processing component configured for processing theapplication. Classifying each block includes classifying a block as aflat block by comparing an intensity range with a threshold anddetermining the intensity range is less than or equal to the threshold.Classifying each block includes classifying a block as a texture/edgeblock by comparing an intensity range with a threshold and determiningthe intensity range is greater than the threshold. The spatial variationinformation comprises an intensity range. Determining the intensityrange of the pixel includes: determining a maximum intensity pixel valuewithin a current block, determining a minimum intensity pixel valuewithin the current block and calculating a spatial variation bysubtracting the minimum intensity pixel value from the maximum intensitypixel value. Classifying each pixel comprises calculating a median of anintensity range within the current block, attributing a pixel in thecurrent block to a first class if the pixel's intensity value is largerthan the median and attributing the pixel in the current block to asecond class if the pixel's intensity value is less than or equal to themedian. Classifying each pixel further comprises comparing a pixel'sclass with a class of neighboring pixels and denoting the pixel as noisyif the pixel's class and the class of the neighboring pixels is thesame. Detecting the dominant edge includes calculating a vectordifference between two pixels. Detecting the dominant edge includes:determining a minimum difference among four directional differences,selecting a maximum difference with a direction orthogonal to theminimum difference direction, calculating an edge difference anddetermining if a pixel belongs to the dominant edge. Generating the baseweights includes calculating the base weights for noisy pixels, edgepixels and texture pixels. Generating the base weights includescalculating the base weights for a texture pixel with flat neighborblocks. Generating the base weights includes adjusting the base weightsfor chrominance blocks. Applying the adaptive enhancement includesapplying sharpness enhancement on denoted edge/texture pixels. Applyingthe adaptive enhancement includes: not applying sharpness enhancement ifa current pixel belongs to a flat block, not applying sharpnessenhancement if the current pixel is a noisy pixel, enhancing sharpnessif the current pixel belongs to a dominant edge and enhancing sharpnessif the current pixel has not been filtered. The device is selected fromthe group consisting of a personal computer, a laptop computer, acomputer workstation, a server, a mainframe computer, a handheldcomputer, a personal digital assistant, a cellular/mobile telephone, asmart appliance, a gaming console, a digital camera, a digitalcamcorder, a camera phone, an iPod®, a video player, a DVDwriter/player, a television and a home entertainment system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flow chart of the method of reducing artifacts andenhancing quality.

FIG. 2 illustrates a block diagram of a center block and neighboringblocks.

FIG. 3 illustrates a representation of a vector-based similaritymeasure.

FIG. 4 illustrates a block diagram of an exemplary computing deviceconfigured to implement the method of reducing artifacts and improvingquality for image and video encoding.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A low complexity encoding artifacts reduction method to generate imageand video with high quality is described herein. Unlike prior artmethods that perform artifact reduction and sharpness enhancementseparately, the two tasks are able to be accomplished in one pass.First, the current block is detected as a flat block or a texture/edgeblock. For the texture/edge blocks, a vector-based dominant edgedetection and pixel intensity classification is combined to determinethe coefficients of a 3×3 filter for each pixel in the current block.For the flat block, the filter coefficients are only determined by theintensity difference between the current pixel and its neighbor pixels.The obtained filter is then applied to this pixel to remove thequantization noise including the ringing noise. After the filtering, anadaptive enhancement is applied. By using the information obtainedduring the filtering process, only the natural edge and texture areenhanced. As a result, better visual quality is obtained compared to theprior art.

Through many years of investigation by researchers around the world, itis acknowledged that the encoding artifacts are content dependent.Various content analysis including spatial variation extraction,dominant edge detection, neighbor condition check and pixelclassification are utilized herein.

The overall scheme of the method of reducing artifacts and enhancingquality is shown in FIG. 1. The spatial intensity variation is analyzedand extracted for each block in the step 100. Based on the extractedspatial variation result, the current block is attributed to either aflat block or a texture/edge block, in the step 102. The informationobtained during the spatial variation extraction is also utilized in theclassification module, in the step 104. In the classification module,every pixel in the current block is classified. The classificationresult is able to be utilized in the subsequent module to help determineif the current pixel belongs to texture or noise. If the current blockis determined as a texture/edge block, dominant edge detection isapplied to every pixel in the current macroblock, in the step 106. Thisdetection is based on the vector-based similarity calculation, thespatial variation information and the quantization value of the currentblock. All of the pixels within the current block are determined eitheras edge pixels or texture pixels.

At the same time of dominant edge detection, the spatial variation ofthe 8-neighbor blocks around the current block is also checked, in thestep 108. The 8-neighbor blocks are illustrated in FIG. 2. If at leastone 8-neighbor block is a very smooth block, then the noise sensitivityis much higher in the current block.

By combining the dominant edge detection and neighbor block checkresult, the base weights of the N×N filter are generated, in the step110. Since the human visual system is not sensitive to the quantizationnoise in the texture area, if the current pixel is determined as atexture pixel, smaller weight is assigned. If the current pixel isdetermined as an edge pixel, a bigger weight is assigned to the pixelsalong the edge. This base weight is then adjusted based on theclassification result and pixel vector difference to determine the finalfilter coefficients.

If the current block is determined as a flat block, there are no edgeand texture pixels within the block. Therefore, edge detection andclassification is unnecessary. The filter coefficients are generated inthe step 112 and are totally based on the pixel difference between thecurrent pixel and its neighboring pixels.

After the filter coefficients are obtained, the pixels in the currentblock are filtered in the step 114 as follows:

$Y_{i,j} = \frac{\sum\limits_{k = {- 1}}^{N}\;{\sum\limits_{\;{l = {- 1}}}^{N}{W_{{i + k},{j + l}} \cdot P_{{i + k},{j + l}}}}}{\sum\limits_{k = {- 1}}^{N}{\sum\limits_{\;{l = {- 1}}}^{N}W_{{i + k},{j + l}}}}$In the above equation, i and j denote position of the pixel to befiltered. P_(i+k,j+l) denotes the decoded pixels that are used in thefiltering process. W_(j+k,j+l) denotes the filter coefficient for thecorresponding pixels and Y_(i,j) denotes the filter result. Under mostcircumstances, N=3 is sufficient for artifact removal. In the following,N=3 is used; however, in some embodiments N is equal to another number.

Then, in the step 116, an adaptive enhancement method is applied toenhance the texture and edge pixels. By utilizing the informationobtained during the filtering process, the noise is differentiated fromthe texture pixels. Therefore, the enhancement is able to avoidamplifying the noise.

As described above, the first stage of the method of reducing artifactsand enhancing quality is spatial variation extraction. The spatialvariation is able to be measured by many different methods—from verycomplicated entropy estimation to simple sub-block pixel intensity meandifference. Since an objective is to obtain information for encodingnoise reduction, the “good enough” low complexity solution is desirable.With this in mind, the pixel intensity range within the current block asthe measure of the spatial variation of each block is used. Theintensity range is able to be obtained by the following procedure:

-   -   Determining the maximum intensity pixel value P_(MAX) within the        current block;    -   Determining the minimum intensity pixel value P_(MIN) within the        current block; and    -   Calculating the spatial variation (or intensity range) as        BK_(range)=P_(MAX)−P_(MIN).

To determine if a block is a flat block or a texture/edge block, theBK_(range) is compared with a threshold T(Q). If the range is largerthan T(Q), the current block is attributed to a texture/edge block.Otherwise, the current block is attributed to a flat block. Here, T(Q)is a function of the quantization parameter. When Q is increased, T(Q)is also increased.

The classification module plays an important role to differentiate thenoise from texture/edge. The following procedure is used to classifyeach pixel in the current block:

-   -   Calculating the median of the intensity range within the current        block as P_(Median)=(P_(MAX)+P_(MIN))/2; and    -   For each Pixel in the current block, attributing them into two        classes using the following procedure:        -   If the pixel's intensity value is larger than P_(Median),            the pixel is attributed to class one; and        -   If the pixel's intensity value is less or equal to            P_(Median), the pixel is attributed to class two.

To determine if a pixel is heavily affected by the encoding artifacts,the classification result of the pixel is compared to its eightneighboring pixels. If all of them share the same class, the pixel isvery likely to be heavily affected by the encoding artifacts and isdenoted as a noisy pixel. Otherwise, the pixel is denoted as atexture/edge pixel. If the pixel is denoted as a texture/edge pixel, thepixel is classified into either a texture or an edge pixel at a laterstage.

The filter weight (coefficients strength) is dependent on the followinginformation: if the current pixel is denoted as a noisy pixel based onthe classification; if the current pixel is located on a dominant edge;or if the filtered pixel pair is similar enough. To obtain the aboveinformation, dominant edge detection is used. There are many methodsdeveloped for edge detection. For reducing artifacts and enhancingquality, good enough edge detection is needed but with very lowcomplexity.

A vector similarity-based dominant edge detection is based on the vectorsimilarity measure which is illustrated by FIG. 3. As shown in thefigure, P(i,j) denotes the current pixel, its difference with its upperleft neighbor pixel P(i−1, j−1) is calculated by the following equation:Diff(Upper_left)=|P(i,j)−P(i−1,j−1)|+|P(i,j−1)−P(i−1,j−2)|+|P(i,j+1)−P(i−1,j)|Similarly, the difference with its other seven neighboring pixels arecalculated by the following:Diff(Lower_right)=|P(i,j)−P(i+1,j+1)|+|P(i,j−1)−P(i+1,j)|+|P(i,j+1)−P(i+1,j+2)|Diff(Upper_right)=|P(i,j)−P(i−1,j+1)|+|P(i,j−1)−P(i−1,j)|+|P(i,j+1)−P(i−1,j+2)|Diff(Upper)=|P(i,j)−P(i−1,j)|+|P(i,j−1)−P(i−1,j−1)|+|P(i,j+1)−P(i−1,j+1)|Diff(Lower_left)=|P(i,j)−P(i+1,j−1)|+|P(i,j−1)−P(i+1,j−2)|+|P(i,j+1)−P(i+1,j)|Diff(Lower)=|P(i,j)−P(i+1,j)|+|P(i,j−1)−P(i+1,j−1)|+|P(i,j+1)−P(i+1,j+1)|Diff(Left)=|P(i,j)−P(i,j−1)|+|P(i−1,j)−P(i−1,j−1)|+|P(i+1,j)−P(i+1,j)−P(i+1,j−1)|Diff(Right)=|P(i,j)−P(i,j+1)|+|P(i−1,j)−P(i−1,j+1)|+|P(i+1,j)−P(i+1,j+1)|Based on the similarity measure between the current pixel and its 8neighboring pixels, the directional difference of vertical direction,horizontal direction, diagonal 45° direction and diagonal 135° directionare calculated by:DIFF_(ver)=Diff(Upper)+Diff(Lower)DIFF_(hor)=Diff(Left)+Diff(Right)DIFF_(dia45)°=Diff(Upper_left)+Diff(Lower_right)+C ₁DIFF_(dia135)°=Diff(Upper_right)+Diff(Lower_left)+C ₁

In the above equations, C₁ is a constant which reflects the fact thatthe human visual system is more sensitive to the vertical and horizontaledge compared to the diagonal edges. With the directionality measure asabove, the dominant edge can be determined. The dominant edge isdetermined by the following procedure:

-   -   Determining the minimum Min(DIFF) among four directional        difference DIFF_(ver), DIFF_(hor), DIFF_(45°) and DIFF_(135°);    -   Selecting the maximum difference with the direction orthogonal        to the direction of Min(DIFF). Then, calculating the edge        difference by:        Edge_Diff=Max(DIFF)−Min(DIFF); and    -   If the following condition is satisfied, the current pixel is        determined to belong to a dominant edge and denoted as an edge        pixel. The edge direction is the direction that generates the        Min(DIFF). Otherwise, the current pixel is determined to belong        to texture and denoted as a texture pixel.        Edge_Diff≧BK _(range)&&Min(DIFF)<BK _(threshold)        For the luminance block, the threshold value is calculated as:

${BK}_{threshold} = \{ \begin{matrix}{T\; 2} & {{BK}_{range} > {T\; 2}} \\{BK}_{range} & {Otherwise}\end{matrix} $For the chrominance block, the threshold value is calculated as:

${BK}_{threshold} = \{ \begin{matrix}{T\; 2} & {{BK}_{range} > {T\; 2}} \\{{BK}_{range}{\operatorname{<<}1}} & {Otherwise}\end{matrix} $With the dominant edge detection and classification results, the filterbase weight is able to be generated as follows:

-   -   If a pixel is determined as a noisy pixel based on the        classification or as an edge pixel, its base weight is        calculated as follows:

$W_{base} = \{ \begin{matrix}16 & {{Noisy}\text{-}{pixel}} \\12 & {{Edge\_ diff} > {1.5\;{BK}_{range}}} \\8 & {{Edge\_ diff} > {BK}_{range}}\end{matrix} $

-   -   If a pixel is determined as a texture pixel, its base weight is        calculated as follows. This means the base weight is 8 when the        neighbor block check returns positive.

$W_{base} = \{ \begin{matrix}8 & {{flat} - {neighbor}} \\0 & {otherwise}\end{matrix} $After the base weight is obtained, the final filter weights arecalculated according to the following equation:

$W_{{i + k},{j + l}} = \{ \begin{matrix}{16,} & {{k = 0},{l = 0}} \\{{W_{base} - \frac{W_{base} \cdot {{DIFF}( {P_{i,j},P_{{i + k},{j + l}}} )}}{{BK}_{threshold}}},} & {{Edge}\text{-}{direction}} \\{{( {W_{base} - \frac{W_{base} \cdot {{DIFF}( {P_{i,j},P_{{i + k},{j + l}}} )}}{{BK}_{threshold}}} )/2},} & {{Other}\text{-}{directions}}\end{matrix} $According to the above equation, the maximum weight of the filteredpixel is 16. The neighbor pixels along the edge direction are heavyweighted. If the above calculation returns a negative weight value, therespective weight is set as zero. If the current pixel is a texturepixel with a flat neighbor, all of its neighbor pixels are treatedequally. That means they all use the calculation for the edge direction.

Since there is not an edge in a flat block, the filtering process isrelatively simple compared to the filtering process for a texture/edgeblock. The filter weights are calculated by the following equation:

$W_{{i + k},{j + l}} = \{ \begin{matrix}16 & {{k = 0},{l = 0}} \\{16 - \frac{16 \cdot {{DIFF}( {P_{i,j},P_{{i + k},{j + l}}} )}}{{BK}_{range}}} & {others}\end{matrix} $

Research has shown that the human visual system prefers a sharperpicture compared to a mild picture under most scenarios. Therefore,sharpness enhancement is included in most consumer image & videoproducts. When the input picture is noise free, the sharpnessenhancement improves the visual quality. The sharpness enhancement makesthe edge more sharp and the texture detail more visible. However, thisenhancement is able to also amplify the noise or encoding artifacts.Sometimes, the picture appears worse after the sharpness enhancement.Even if the artifact reduction made the encoding artifacts unnoticeable,the sharpness enhancement is able to make them visible again. Therefore,the sharpness enhancement method that is able to avoid artifactsamplification is desirable.

In this application, the blocks within the picture have been classifiedinto either a flat block or a texture/edge block. Within thetexture/edge block, the pixels are denoted as an edge pixel, a noisypixel or a texture pixel. By using this information, the followingprocedure is able to be used to enhance the picture:

-   -   If the current pixel belongs to a flat block, no sharpness        enhancement is applied;    -   If the current pixel is denoted as a noisy pixel, no sharpness        enhancement is applied;    -   If the current pixel belongs to a dominant edge, the sharpness        is enhanced; and    -   If the current pixel has not been filtered, which means        W_(base)=0, the sharpness is enhanced.        By using the above procedure, the artifacts amplification is        avoided.

FIG. 4 illustrates a block diagram of an exemplary computing device 400configured to implement the method of reducing artifacts and improvingquality for image and video encoding. The computing device 400 is ableto be used to acquire, store, compute, communicate and/or displayinformation such as images and videos. For example, a computing device400 acquires a video, and then the method for reducing artifacts andimproving image/video encoding quality improves the appearance of theimage/video. In general, a hardware structure suitable for implementingthe computing device 400 includes a network interface 402, a memory 404,a processor 406, I/O device(s) 408, a bus 410 and a storage device 412.The choice of processor is not critical as long as a suitable processorwith sufficient speed is chosen. The memory 404 is able to be anyconventional computer memory known in the art. The storage device 412 isable to include a hard drive, CDROM, CDRW, DVD, DVDRW, flash memory cardor any other storage device. The computing device 400 is able to includeone or more network interfaces 402. An example of a network interfaceincludes a network card connected to an Ethernet or other type of LAN.The I/O device(s) 408 are able to include one or more of the following:keyboard, mouse, monitor, display, printer, modem, touchscreen, buttoninterface and other devices. Artifact reduction and quality improvementapplication(s) 430 used to perform the method of reducing artifacts andimproving quality of images/video are likely to be stored in the storagedevice 412 and memory 404 and processed as applications are typicallyprocessed. More or less components shown in FIG. 4 are able to beincluded in the computing device 400. In some embodiments, artifactreduction and quality improvement hardware 420 is included. Although thecomputing device 400 in FIG. 4 includes applications 430 and hardware420 for artifact reduction and quality improvement, the artifactreduction and quality improvement method is able to be implemented on acomputing device in hardware, firmware, software or any combinationthereof.

In some embodiments, the artifact reduction and quality improvementapplication(s) 430 include several applications and/or modules. In someembodiments, the artifact reduction and quality improvementapplication(s) 430 include a spatial variation extraction module 432, aflat or texture/edge block detection module 434, a classification module436, a vector-based dominant edge detection module 438, a neighbor blockcheck module 440, a base weight generation module 442, a filtercoefficients generation module 444, a pixel-based filtering module 446and an adaptive enhancement module 448.

As described above, the spatial variation extraction module 432 analyzesthe spatial intensity variation for each block and extracts a spatialvariation result. Based on the extracted spatial variation result, theflat or texture/edge block detection module 434 determines if the blockis a flat block or a texture/edge block. The classification module 436also uses the information obtained by the spatial variation extractionmodule 432 and classifies every pixel in the current block. Theclassification result is utilized to help determine if the current pixelbelongs to texture or noise. The vector-based dominant edge detectionmodule 438 applies vector-based dominant edge detection to every pixelin the current macroblock, if the block is determined to be atexture/edge block. The neighbor block check module 440 checks thespatial variation of the 8-neighbor blocks. The base weight generationmodule 442 generates base weights of the filter by combining thedominant edge detection and neighbor block check result. The filtercoefficients generation module 444 generates filter coefficientsdepending on specified criteria. The pixel-based filtering module 446filters pixels in the current block. The adaptive enhancement module 448enhances the texture and edge pixels.

Examples of suitable computing devices include a personal computer, alaptop computer, a computer workstation, a server, a mainframe computer,a handheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, an iPod®, a video player, a DVDwriter/player, a television, a home entertainment system or any othersuitable computing device.

To utilize the artifact reduction and quality improvement method, acomputing device operates as usual, but the video/image processing isimproved in that the image or video quality is improved and artifactsare reduced. The utilization of the computing device from the user'sperspective is similar or the same as one that uses standard operation.For example, the user still simply turns on a digital camcorder and usesthe camcorder to record a video. The artifact reduction and qualityimprovement method is able to automatically improve the quality of thevideo without user intervention. The artifact reduction and qualityimprovement method is able to be used anywhere that requires imageand/or video processing. Many applications are able to utilize theartifact reduction and quality improvement method.

In operation, the artifact reduction and quality improvement methodenables many improvements related to image/video processing. Byperforming artifact reduction and sharpness enhancement in one pass,better image/video results are obtained. Furthermore, the specificimplementation of artifact reduction and sharpness enhancement is animproved implementation over the prior art.

The present invention has been described in terms of specificembodiments incorporating details to facilitate the understanding ofprinciples of construction and operation of the invention. Suchreference herein to specific embodiments and details thereof is notintended to limit the scope of the claims appended hereto. It will bereadily apparent to one skilled in the art that other variousmodifications may be made in the embodiment chosen for illustrationwithout departing from the spirit and scope of the invention as definedby the claims.

What is claimed is:
 1. A method of reducing image/video encodingartifacts and improving quality within a computing device, the methodcomprising: a. classifying each block in an image/video using pixelintensity range-based block classification; b. detecting a dominant edgeusing vector difference-based dominant edge detection; c. generatingcontent adaptive base weights using content adaptive base weightgeneration; d. generating content adaptive filter weights using contentadaptive final filter weight generation; and e. enhancing textures andedges using adaptive texture and edge enhancement.
 2. The method ofclaim 1 wherein classifying each block includes classifying a block as aflat block by comparing an intensity range with a threshold anddetermining the intensity range is less than or equal to the threshold.3. The method of claim 1 wherein classifying each block includesclassifying a block as a texture/edge block by comparing an intensityrange with a threshold and determining the intensity range is greaterthan the threshold.
 4. The method of claim 1 wherein an intensity rangeof the pixel intensity range-based block classification includes: a.determining a maximum intensity pixel value within a current block; b.determining a minimum intensity pixel value within the current block;and c. calculating a spatial variation by subtracting the minimumintensity pixel value from the maximum intensity pixel value.
 5. Themethod of claim 1 further comprising classifying each pixel in a currentblock.
 6. The method of claim 5 wherein classifying each pixelcomprises: a. calculating a median of an intensity range within thecurrent block; b. attributing a pixel in the current block to a firstclass if the pixel's intensity value is larger than the median; and c.attributing the pixel in the current block to a second class if thepixel's intensity value is less than or equal to the median.
 7. Themethod of claim 6 wherein classifying each pixel further comprisescomparing a pixel's class with a class of neighboring pixels anddenoting the pixel as noisy if the pixel's class and the class of theneighboring pixels is the same.
 8. The method of claim 1 wherein thevector difference-based dominant edge detection includes calculating avector difference between two pixels.
 9. The method of claim 1 whereinthe vector difference-based dominant edge detection includes: a.determining a minimum difference among four directional differences; b.selecting a maximum difference with a direction orthogonal to theminimum difference direction; c. calculating an edge difference; and d.determining if a pixel belongs to a dominant edge.
 10. The method ofclaim 1 wherein the content adaptive base weight generation includescalculating base weights for noisy pixels, edge pixels and texturepixels.
 11. The method of claim 1 wherein the content adaptive baseweight generation includes calculating base weights for a texture pixelwith flat neighbor blocks.
 12. The method of claim 1 wherein the contentadaptive base weight generation includes adjusting base weights forchrominance blocks.
 13. The method of claim 1 wherein the contentadaptive final filter weight generation includes calculating a finalweight for a texture/edge block.
 14. The method of claim 1 wherein thecontent adaptive final filter weight generation includes calculating afinal weight for a flat block.
 15. The method of claim 1 wherein theadaptive texture and edge enhancement includes applying sharpnessenhancement on denoted edge/texture pixels.
 16. The method of claim 1wherein the adaptive texture and edge enhancement includes: a. notapplying sharpness enhancement if a current pixel belongs to a flatblock; b. not applying sharpness enhancement if the current pixel is anoisy pixel; c. enhancing sharpness if the current pixel belongs to adominant edge; and d. enhancing sharpness if the current pixel has notbeen filtered.
 17. The method of claim 1 wherein the computing device isselected from the group consisting of a personal computer, a laptopcomputer, a computer workstation, a server, a mainframe computer, ahandheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, an iPod®, a video player, a DVDwriter/player, a television and a home entertainment system.
 18. Amethod of reducing image/video encoding artifacts and improving qualitywithin a computing device, the method comprising: a. classifying eachblock within an image/video; b. determining filter coefficients for eachpixel in a current block, wherein determining the filter coefficientsincludes docketing a dominant edge and classifying pixel intensity for atexture/edge block; c. filtering each pixel with the filter coefficientsto remove noise; and d. applying an adaptive enhancement to enhancetexture and edge pixels.
 19. The method of claim 18 wherein classifyingeach block includes classifying a block as one of a flat block and atexture/edge block.
 20. The method of claim 19 wherein classifying eachblock includes classifying a block as the flat block by comparing anintensity range with a threshold and determining the intensity range isless than or equal to the threshold.
 21. The method of claim 19 whereinclassifying each block includes classifying a block as the texture/edgeblock by comparing an intensity range with a threshold and determiningthe intensity range is greater than the threshold.
 22. The method ofclaim 21 wherein determining the intensity range of the pixel includes:a. determining a maximum intensity pixel value within a current block;b. determining a minimum intensity pixel value within the current block;and c. calculating a spatial variation by subtracting the minimumintensity pixel value from the maximum intensity pixel value.
 23. Themethod of claim 18 further comprising classifying each pixel comprising:a. calculating a median of an intensity range within the current block;b. attributing a pixel in the current block to a first class if thepixel's intensity value is larger than the median; and c. attributingthe pixel in the current block to a second class if the pixel'sintensity value is less than or equal to the median.
 24. The method ofclaim 23 wherein classifying each pixel further comprises comparing apixel's class with a class of neighboring pixels and denoting the pixelas noisy if the pixel's class and the class of the neighboring pixels isthe same.
 25. The method of claim 18 wherein determining the filtercoefficients includes determining intensity difference between thecurrent pixel and neighboring pixels for a flat block.
 26. The method ofclaim 18 wherein detecting the dominant edge includes calculating avector difference between two pixels.
 27. The method of claim 18 whereindetecting the dominant edge includes: a. determining a minimumdifference among four directional differences; b. selecting a maximumdifference with a direction orthogonal to the minimum differencedirection; c. calculating an edge difference; and d. determining if apixel belongs to the dominant edge.
 28. The method of claim 18 whereinapplying the adaptive enhancement includes applying sharpnessenhancement on denoted edge/texture pixels.
 29. A method of reducingimage/video encoding artifacts and improving quality within a computingdevice, the method comprising: a. classifying each block within animage/video; b. determining filter coefficients for each pixel in acurrent block; c. filtering each pixel with the filter coefficients toremove noise; and d. applying an adaptive enhancement to enhance textureand edge pixels, wherein applying the adaptive enhancement includes: i.not applying sharpness enhancement if a current pixel belongs to a flatblock; ii. not applying sharpness enhancement if the current pixel is anoisy pixel; iii. enhancing sharpness if the current pixel belongs to adominant edge; and iv. enhancing sharpness if the current pixel has notbeen filtered.
 30. The method of claim 18 wherein the computing deviceis selected from the group consisting of a personal computer, a laptopcomputer, a computer workstation, a server, a mainframe computer, ahandheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, an iPod®, a video player, a DVDwriter/player, a television and a home entertainment system.
 31. Asystem for reducing image/video encoding artifacts and improving qualityimplemented with a computing device, the system comprising: a. a spatialvariation extraction module configured for extracting spatial variationinformation for each block of an image/video; b. a block detectionmodule operatively coupled to the spatial variation extraction module,the block detection module configured for classifying each block usingthe spatial variation information; c. a classification moduleoperatively coupled to the spatial variation extraction module, theclassification module configured for classifying each pixel in a currentblock using the spatial variation information; d. a dominant edgedetection module operatively coupled to the block detection module, thedominant edge detection module configured for detecting a dominant edge;e. a neighbor block check module operatively coupled to the dominantedge detection module, the neighbor block check module configured forchecking spatial variation of neighboring blocks of the current block;f. a base weight generation module operatively coupled to the neighborblock check module, the base weight generation module configured forgenerating base weights by combining results from detecting the dominantedge and checking the spatial variation of neighboring blocks; g. afilter coefficients generation module operatively coupled to the baseweight generation module, the filter coefficients generation moduleconfigured for generating filter coefficients; h. a pixel-basedfiltering module operatively coupled to the filter coefficientsgeneration module, the pixel-based filtering module configured forfiltering pixels; and i. an adaptive enhancement module operativelycoupled to the pixel-based filtering module, the adaptive enhancementmodule configured for applying an adaptive enhancement to enhancetexture and edge pixels.
 32. The system of claim 31 wherein the blockdetection module classifies a block as a flat block by comparing anintensity range with a threshold and determining the intensity range isless than or equal to the threshold.
 33. The system of claim 31 whereinthe block detection module classifies a block as a texture/edge block bycomparing an intensity range with a threshold and determining theintensity range is greater than the threshold.
 34. The system of claim31 wherein the spatial variation information comprises an intensityrange.
 35. The system of claim 34 wherein determining the intensityrange of the pixel includes: a. determining a maximum intensity pixelvalue within a current block; b. determining a minimum intensity pixelvalue within the current block; and c. calculating a spatial variationby subtracting the minimum intensity pixel value from the maximumintensity pixel value.
 36. The system of claim 31 wherein theclassification module classifies each pixel by: a. calculating a medianof an intensity range within the current block; b. attributing a pixelin the current block to a first class if the pixel's intensity value islarger than the median; and c. attributing the pixel in the currentblock to a second class if the pixel's intensity value is less than orequal to the median.
 37. The system of claim 36 wherein classifying eachpixel further comprises comparing a pixel's class with a class ofneighboring pixels and denoting the pixel as noisy if the pixel's classand the class of the neighboring pixels is the same.
 38. The system ofclaim 31 wherein the dominant edge detector module detects the dominantedge by calculating a vector difference between two pixels.
 39. Thesystem of claim 31 wherein the dominant edge detector module detects thedominant edge by: a. determining a minimum difference among fourdirectional differences; b. selecting a maximum difference with adirection orthogonal to the minimum difference direction; c. calculatingan edge difference; and d. determining if a pixel belongs to thedominant edge.
 40. The system of claim 31 wherein the base weightgeneration module calculates the base weights for noisy pixels, edgepixels and texture pixels.
 41. The system of claim 31 wherein the baseweight generation module calculates the base weights for a texture pixelwith flat neighbor blocks.
 42. The system of claim 31 wherein the baseweight generation module adjusts the base weights for chrominanceblocks.
 43. The system of claim 31 wherein the adaptive enhancementmodules applies sharpness enhancement on denoted edge/texture pixels.44. The system of claim 31 wherein applying the adaptive enhancementincludes: a. not applying sharpness enhancement if a current pixelbelongs to a flat block; b. not applying sharpness enhancement if thecurrent pixel is a noisy pixel; c. enhancing sharpness if the currentpixel belongs to a dominant edge; and d. enhancing sharpness if thecurrent pixel has not been filtered.
 45. The system of claim 31 whereinthe spatial variation extraction module, the block detection module, theclassification module, the dominant edge detection module, the neighborblock check module, the base weight generation module, the filtercoefficients generation module, the pixel-based filtering module and theadaptive enhancement module are implemented in software.
 46. The systemof claim 31 wherein the spatial variation extraction module, the blockdetection module, the classification module, the dominant edge detectionmodule, the neighbor block check module, the base weight generationmodule, the filter coefficients generation module, the pixel-basedfiltering module and the adaptive enhancement module are implemented inhardware.
 47. The system of claim 31 wherein the spatial variationextraction module, the block detection module, the classificationmodule, the dominant edge detection module, the neighbor block checkmodule, the base weight generation module, the filter coefficientsgeneration module, the pixel-based filtering module and the adaptiveenhancement module are implemented in at least one of software, firmwareand hardware.
 48. The system of claim 31 wherein the computing device isselected from the group consisting of a personal computer, a laptopcomputer, a computer workstation, a server, a mainframe computer, ahandheld computer, a personal digital assistant, a cellular/mobiletelephone, a smart appliance, a gaming console, a digital camera, adigital camcorder, a camera phone, an iPod®, a video player, a DVDwriter/player, a television and a home entertainment system.
 49. Adevice comprising: a. a memory for storing an application, theapplication configured for: i. extracting spatial variation informationfor each block of an image/video; ii. classifying each block using thespatial variation information; iii. classifying each pixel in a currentblock using the spatial variation information; iv. detecting a dominantedge; v. checking spatial variation of neighboring blocks of the currentblock; vi. generating base weights by combining results from detectingthe dominant edge and checking the spatial variation of neighboringblocks; vii. generating filter coefficients; viii. filtering pixelsusing the filter coefficients; and ix. applying an adaptive enhancementto enhance texture and edge pixels; and b. a processing componentcoupled to the memory, the processing component configured forprocessing the application.
 50. The device of claim 49 whereinclassifying each block includes classifying a block as a flat block bycomparing an intensity range with a threshold and determining theintensity range is less than or equal to the threshold.
 51. The deviceof claim 49 wherein classifying each block includes classifying a blockas a texture/edge block by comparing an intensity range with a thresholdand determining the intensity range is greater than the threshold. 52.The device of claim 49 wherein the spatial variation informationcomprises an intensity range.
 53. The device of claim 52 whereindetermining the intensity range of the pixel includes: a. determining amaximum intensity pixel value within a current block; b. determining aminimum intensity pixel value within the current block; and c.calculating a spatial variation by subtracting the minimum intensitypixel value from the maximum intensity pixel value.
 54. The device ofclaim 49 wherein classifying each pixel comprises: a. calculating amedian of an intensity range within the current block; b. attributing apixel in the current block to a first class if the pixel's intensityvalue is larger than the median; and c. attributing the pixel in thecurrent block to a second class if the pixel's intensity value is lessthan or equal to the median.
 55. The device of claim 54 whereinclassifying each pixel further comprises comparing a pixel's class witha class of neighboring pixels and denoting the pixel as noisy if thepixel's class and the class of the neighboring pixels is the same. 56.The device of claim 49 wherein detecting the dominant edge includescalculating a vector difference between two pixels.
 57. The device ofclaim 49 wherein detecting the dominant edge includes: a. determining aminimum difference among four directional differences; b. selecting amaximum difference with a direction orthogonal to the minimum differencedirection; c. calculating an edge difference; and d. determining if apixel belongs to the dominant edge.
 58. The device of claim 49 whereingenerating the base weights includes calculating the base weights fornoisy pixels, edge pixels and texture pixels.
 59. The device of claim 49wherein generating the base weights includes calculating the baseweights for a texture pixel with flat neighbor blocks.
 60. The device ofclaim 49 wherein generating the base weights includes adjusting the baseweights for chrominance blocks.
 61. The device of claim 49 whereinapplying the adaptive enhancement includes applying sharpnessenhancement on denoted edge/texture pixels.
 62. The device of claim 49wherein applying the adaptive enhancement includes: a. not applyingsharpness enhancement if a current pixel belongs to a flat block; b. notapplying sharpness enhancement if the current pixel is a noisy pixel; c.enhancing sharpness if the current pixel belongs to a dominant edge; andd. enhancing sharpness if the current pixel has not been filtered. 63.The device of claim 49 wherein the device is selected from the groupconsisting of a personal computer, a laptop computer, a computerworkstation, a server, a mainframe computer, a handheld computer, apersonal digital assistant, a cellular/mobile telephone, a smartappliance, a gaming console, a digital camera, a digital camcorder, acamera phone, an iPod®, a video player, a DVD writer/player, atelevision and a home entertainment system.